Hugues Aschard

Jul 30, 2024 | nature.com | Chen Wang |Ying Wei |Hugues Aschard

AbstractGenome-wide association studies (GWAS) for biomarkers important for clinical phenotypes can lead to clinically relevant discoveries. Conventional GWAS for quantitative traits are based on simplified regression models modeling the conditional mean of a phenotype as a linear function of genotype.

Aug 5, 2023 | nature.com | Clara Albiñana |Hugues Aschard |Cynthia Bulik |Jakob Grove |David M. Hougaard |Thomas Werge | +5 more

AbstractThe predictive performance of polygenic scores (PGS) is largely dependent on the number of samples available to train the PGS. Increasing the sample size for a specific phenotype is expensive and takes time, but this sample size can be effectively increased by using genetically correlated phenotypes. We propose a framework to generate multi-PGS from thousands of publicly available genome-wide association studies (GWAS) with no need to individually select the most relevant ones.